Intelligent textile management system

Abstract

The invention provides an intelligent textile management system, relates to the technical field of textile, and can improve the technical problems of resource waste or production bottleneck caused by the disjunction of a production plan and an inventory and the lack of full consideration of the inventory state in the formulation of the production plan in related technologies. The management system comprises an initial plan module used for constructing a basic plan according to historical order data and market trends, and a textile production line performs production according to the basic plan; the inventory module is coupled with the initial planning module, and the inventory module is used for predicting the number of producible products according to the number of inventory raw materials and the mapping table, and obtaining a target plan according to the number of producible products, the number of inventory products and the basic plan, the mapping table is used for mapping the mapping relation between the raw material quantity and the producible product quantity; and the production control module is coupled with the inventory module, and the production control module is used for controlling the textile production line to carry out production according to the target plan.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of textile technology, and in particular, to an intelligent textile management system. Background Art

[0002] In existing textile fabric production management systems, production planning is usually based on historical data and market trend analysis. However, these methods have several limitations, which lead to inaccurate production planning and slow response. For example, production planning is out of touch with inventory. Production planning often does not fully consider inventory status, including the inventory quantity of raw materials and finished products, resulting in resource waste or production bottlenecks. Summary of the invention

[0003] The embodiment of the present application provides an intelligent textile management system for improving technical problems in related technologies, such as the disconnection between production planning and inventory, the formulation of production plans often not fully considering inventory status, resulting in waste of resources or production bottlenecks.

[0004] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0005] An embodiment of the present application provides an intelligent textile management system, which is applied to a textile production line. The management system includes: an initial planning module, which is used to construct a basic plan based on historical order data and market trends, and the textile production line produces according to the basic plan; an inventory module, which is coupled to the initial planning module, and is used to predict the number of products that can be produced based on the inventory raw material quantity and a mapping table, and obtain the target plan based on the number of products that can be produced, the inventory product quantity and the basic plan, and the mapping table is used to map the mapping relationship between the raw material quantity and the number of products that can be produced; a production control module, which is coupled to the inventory module, and is used to control the textile production line to produce according to the target plan.

[0006] In one possible implementation, the target plan is obtained based on the number of products that can be produced, the number of products in stock, and the basic plan, including: obtaining the number of products to be determined in the target plan based on the number of products in the initial plan and the number of products in stock; obtaining the number of safety stocks and the output capacity of the textile production line; and obtaining the actual number of products in the target plan based on the number of products to be determined, the number of safety stocks, and the output capacity of the textile production line.

[0007] In a possible implementation, the production control module includes: a sampling unit, the sampling unit is used to collect a first parameter and a second parameter, the first parameter is the working parameter of the hot rolling mill when the hot rolling mill fails, and the second parameter is the state parameter of the textile when the hot rolling mill fails; a modulation unit, the modulation unit is electrically connected to the sampling unit, the modulation unit generates a modulation coefficient according to the first parameter, the second parameter and the durability of the textile, and the modulation coefficient is used to modulate the working parameters of the hot rolling mill after the failure is restored; a management unit, the management unit is electrically connected to the modulation unit, and the management unit is used to control the operation of the hot rolling mill according to the modulated working parameters.

[0008] In a possible implementation, the first parameter includes a fault duration of the hot calender, a temperature of a pressure roller, a pressure of the pressure roller on the textile, and a tension of the hot calender on the textile.

[0009] In a possible implementation, the second parameter includes the initial humidity, humidity change rate, and final humidity of the textile at the time of failure.

[0010] In a possible implementation, the modulation unit generates a modulation coefficient according to the first parameter, the second parameter, and the durability of the textile, including: generating the first tension coefficient based on the first parameter, the second parameter, and a first tension coefficient formula, wherein the first tension coefficient formula is:

[0011]

[0012] Among them, CF 1 is the first tension coefficient, T f is the tension of the hot rolling mill on the textile, T is the temperature of the pressing roller, P f is the tension of the hot rolling mill on the textile, H i is the initial humidity of the textile, H f is the final humidity of the textile, H r is the humidity change rate of the textile, D is the fault duration, and θ, η, and Period are coefficients.

[0013] In one possible implementation, the production control module also includes a processing unit, which is used to perform the following steps: establishing a mapping table of the first parameter, the second parameter, and the quality degree of the textile; and obtaining the quality degree of the current batch of textiles based on the first parameter, the second parameter, and the mapping table of the current batch.

[0014] In a possible implementation, the first parameter includes the failure duration of the hot rolling mill, the temperature of the pressure roller, the pressure of the pressure roller on the fiber material, and the tension of the hot rolling mill on the fiber material; the second parameter includes the initial humidity of the fiber material, the humidity change rate, and the final humidity.

[0015] In a possible implementation, the method of obtaining the quality degree of the current batch of textiles based on the first parameter, the second parameter and the mapping table of the current batch includes: obtaining the matching degree of each parameter in the first parameter and the second parameter; obtaining the absolute value of the difference between each parameter in the first parameter and the second parameter of the current batch and each parameter in the first parameter and the second parameter of the historical batch; obtaining a similarity evaluation value based on the absolute value of the difference and the matching degree, and using the quality degree of the textiles of the historical batch corresponding to the maximum similarity evaluation value as the quality degree of the textiles of the current batch. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the structure of an intelligent management system provided for some embodiments of the present application;

[0017] Figure 2 A schematic diagram of the structure of a textile production line provided for some embodiments of the present application;

[0018] Figure 3 A schematic diagram of the structure of a management system and a textile production line provided for some embodiments of the present application;

[0019] Figure 4 A schematic diagram of the structure of a production control module provided for some embodiments of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0021] In the following, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0022] In addition, in the present application, directional terms such as "up", "down", "left" and "right" may be defined including but not limited to the orientation relative to the schematic placement of components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly according to changes in the orientation of the components in the drawings.

[0023] In this application, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, the term "electrical connection" can be a way of achieving electrical connection for signal transmission.

[0024] As used herein, “about,” “substantially,” or “approximately” includes the stated value and reference values ​​that are within an acceptable range of deviation from the particular value, where the acceptable range of deviation is as determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement system).

[0025] In the embodiments of the present application, the words "exemplarily" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

[0026] In existing textile fabric production management systems, production planning is usually based on historical data and market trend analysis. However, these methods have several limitations, which lead to inaccurate production planning and slow response. For example, production planning is out of touch with inventory. Production planning often does not fully consider inventory status, including the inventory quantity of raw materials and finished products, resulting in resource waste or production bottlenecks.

[0027] The embodiment of the present application provides an intelligent textile management system 100, which is used to improve the technical problems in related technologies, such as the disconnection between production plan and inventory, the formulation of production plan often does not fully consider the inventory status, resulting in resource waste or production bottlenecks.

[0028] like Figure 1 As shown, an embodiment of the present application provides an intelligent textile management system 100. The intelligent textile management system 100 is applied to a textile production line 200, and the management system 100 includes an initial planning module 110, an inventory module 120 and a production control module 130.

[0029] The initial planning module 110 is used to construct a basic plan based on historical order data and market trends, and the production plan includes product quantities.

[0030] Exemplarily, the initial planning module 110 has a built-in prediction model, which is:

[0031]

[0032] Among them, Y 1 is the number of products in the basic plan, H s is the historical sales data, SF is the seasonal factor, which is used to reflect the coefficient of seasonal changes in product sales. i is the ith seasonal factor, n is the number of seasonal cycles, P IF is the promotion impact factor, which is used to characterize the expected impact of promotion activities. It can be a qualitative score or the sales growth percentage of historical promotions. TF It is a market trend factor, which is used to characterize the quantitative indicators of market growth or decline, such as industry growth rate, E CF is an economic condition factor, which is used to characterize the impact of macroeconomic indicators, such as GDP growth rate, C IF Competitor influence factor, a quantitative indicator used to characterize competitor behavior, such as market share, P EC It is the price elasticity coefficient, which is used to characterize the impact of price changes on demand and can usually be calculated using historical price and sales data.

[0033] As another example, the ratio of the order data (sales volume) at the current time to the order data at the same historical time is obtained, and the ratio is multiplied by the order data at the next time point in the historical data to obtain the order data at the next time point, and the data is used as the basic plan.

[0034] The inventory module 120 is coupled to the initial planning module 110 , and is used to monitor the inventory status and construct a target plan according to the inventory raw material quantity, the inventory product quantity and the basic plan.

[0035] The inventory module 120 constructs a target plan according to the inventory raw material quantity, the inventory product quantity and the basic plan, including:

[0036] S100: Obtain the quantity of the raw materials in stock and the quantity of the products in stock.

[0037] S200. Predicting the number of products that can be produced based on the inventory raw material quantity and a mapping table, wherein the mapping table is used to map the mapping relationship between the raw material quantity and the number of products that can be produced.

[0038] The mapping table can be obtained based on past production data, such as being constructed based on the past quantity of raw materials and the quantity of products that can be produced based on the quantity of raw materials.

[0039] S300, obtaining the target plan according to the number of products that can be produced, the number of products in stock and the initial generation plan. Exemplarily, the number of products to be determined in the target plan is obtained according to the production plan formula:

[0040] Y 2 =Y 1 ×η-Inv-Ic

[0041] Among them, Y 2 is the number of products to be determined in the target plan, η is the adjustment coefficient considering production fluctuations, Inv is the number of products in stock, and Ic is the number of products that can be produced based on the number of raw materials in stock.

[0042] The amount of safety stock is obtained according to the safety stock formula. Safety stock refers to the additional inventory held to cope with uncertain factors (such as demand fluctuations, supply delays, unstable production, etc.):

[0043]

[0044] Among them, K is the quantity of safety stock, P is the safety service level (a coefficient determined based on the service level target), λ is the standard deviation, which is used to characterize the volatility of demand, and T is the production cycle.

[0045] Safety service level is a metric used when calculating safety stock and is based on the risk of stockouts that a business is willing to take. It is a calculation used to determine the amount of safety stock required to achieve SLT (e.g. 95% of orders are delivered within the specified time).

[0046] For example, if a business wants its service level target to be 95%, meaning that 95% of the time there is enough inventory to meet customer demand, then the safety service level will be set based on this target. If historical data shows that 99% of the time there is enough inventory, then in order to achieve the 95% service level target, the business may not need as much safety stock. However, if historical data shows that only 90% of the time there is enough inventory, the business may need to increase safety stock to reduce the risk of stockouts.

[0047] Get the actual product quantity Y in the target plan 3 : Y 3 =min(Y2+K, Cap), where Cap is the maximum output capacity of the textile production line 200.

[0048] The production control module 130 is coupled to the inventory module 120 , and the production control module 130 is used to control the textile production line 200 to perform production according to the target plan.

[0049] In this way, the forecasting model constructed by the initial planning module 110 using historical order data and market trends can more accurately predict future demand. The inventory module 120 ensures that the production plan can be adjusted based on the latest inventory status by monitoring the inventory status, reducing production decision errors caused by outdated inventory data, and through the precise matching of production plans and inventory status, it can reduce the waste of resources caused by overproduction, such as excessive use of raw materials, human resources and machinery and equipment. The calculation of safety stock takes into account demand fluctuations and supply uncertainties, so that a reasonable inventory level can be set to reduce inventory backlogs and out-of-stock risks.

[0050] like Figure 2 As shown, in some embodiments, the textile production line 200 includes a dyeing machine 210, a hot rolling machine 220, a shaping machine 230 and a packaging machine 240, wherein the dyeing machine 210 is used to dye the textiles, the hot rolling machine 220 is used to perform hot rolling on the textiles, the shaping machine 230 is used to shape the textiles, and the packaging machine 240 is used to package the textiles into products.

[0051] like Figure 3 , Figure 4 As shown, the production control module 130 is also used to control the dyeing machine 210 , the hot rolling machine 220 , the shaping machine 230 and the packaging machine 240 .

[0052] The hot rolling mill 220 applies heat and pressure to the textile to improve the flatness and stability of the textile to meet the requirements of subsequent processes. The operation of the hot rolling mill 220 is usually required to be normal and continuous to ensure the quality and performance of the textile material. However, the actual production process may be stopped due to a malfunction. For example, if an unexpected situation (such as power outage) occurs, causing the hot rolling mill 220 to malfunction, this may affect the effect of its heat setting, and further affect the texture and durability of the textile.

[0053] Although existing hot rolling technology is capable of controlling variables such as temperature and pressure during normal production processes, there are no adequate countermeasures for the potential impact of unexpected interruptions of the hot rolling mill 220 on the performance of textiles.

[0054] To improve the above problems, an embodiment of the present application provides an intelligent textile management system 100. In the management system 100, the control module includes a sampling unit 131, a modulation unit 132 and a management unit 133.

[0055] The sampling unit 131 is used to sample a first parameter and a second parameter, wherein the first parameter is an operating parameter of the hot rolling mill 220 when the hot rolling mill 220 fails, and the second parameter is a state parameter of the textile when the hot rolling mill 220 fails.

[0056] The sampling unit 131 can obtain the above parameters through multiple sensors. For example, the sampling unit 131 can be connected to a temperature sensor, a pressure sensor, a timer, a tension sensor, etc., which are arranged on the hot rolling mill 220. In order for the management system 100 to be able to operate normally when the hot rolling mill 220 is powered off, the management system 100 and the sensors connected thereto can be powered by a power source different from that of the hot rolling mill 220, such as a backup power supply.

[0057] The modulation unit 132 is electrically connected to the sampling unit 131, and the modulation unit 132 generates a modulation coefficient according to the first parameter, the second parameter, and the durability of the textile, and the modulation coefficient is used to modulate the working parameters of the hot rolling mill 220 after the failure is restored. The management unit 133 is electrically connected to the modulation unit 132, and the management unit 133 is used to control the operation of the hot rolling mill 220 after the failure is restored according to the modulated working parameters.

[0058] It should be noted that durability may include one or more parameters of abrasion resistance, color fastness, tensile and compression resistance, fatigue resistance, environmental adaptability, etc. When evaluating the durability of textiles, the evaluation parameters may be selected according to needs.

[0059] Exemplarily, the first parameters include the failure duration of the hot calender 220, the temperature of the pressure roller, the pressure of the pressure roller on the textile, and the tension of the hot calender 220 on the textile. The second parameters include the initial humidity, humidity change rate, and final humidity of the textile at the time of the failure.

[0060] In some embodiments, the modulation coefficient includes a first tension coefficient, and the modulation unit 132 generates the modulation coefficient according to the first parameter, the second parameter, and the durability of the textile, including:

[0061] The first tension coefficient is generated based on the first parameter, the second parameter and a first tension coefficient formula, where the first tension coefficient formula is:

[0062]

[0063] Among them, CF 1 is the first tension coefficient, T f is the tension of the hot rolling mill 220 on the textile, T is the temperature of the pressing roller, P f is the tension of the hot rolling mill 220 on the textile, H iis the initial humidity of the textile, H f is the final humidity of the textile, H r is the humidity change rate of the textile, D is the fault duration, and θ, η, and Period are coefficients.

[0064] In this way, the sensitivity to the relationship between tension and temperature and pressure can be enhanced, so that the prediction method (model) can better respond to small changes in these parameters, and by introducing a constant (Period) related to the production cycle, the model can take into account the cyclical changes in the production process.

[0065] Exemplarily, the modulation unit 132 generates a modulation coefficient according to the first parameter, the second parameter and the durability of the textile, including:

[0066] The first time coefficient is generated based on the first parameter, the second parameter and the first tension coefficient formula, where the first time coefficient formula is:

[0067]

[0068] Among them, CF 2 is the first time coefficient, T f is the tension of the hot rolling mill 220 on the textile, T is the temperature of the pressing roller, P f is the tension of the hot rolling mill 220 on the textile, H i is the initial humidity of the textile, H f is the final humidity of the textile, D is the fault duration, α is the coefficient, and τ is the time constant, which is used to adjust the rate at which the integration kernel changes over time.

[0069] In this way, the continuous effect of temperature on the moisture balance can be considered throughout the failure period, providing an accurate measure of the cumulative effect at one time, taking into account the dynamic balance of temperature, humidity and pressure over time, which helps to more accurately predict the changes in the textile state during and after failure.

[0070] The modulation unit 132 generates a modulation coefficient according to the first parameter, the second parameter and the durability of the textile, including:

[0071] The first time coefficient is generated based on the first parameter, the second parameter and the first tension coefficient formula, and the first pressure coefficient formula is:

[0072]

[0073] Among them, CF 3 is the first pressure coefficient, T f is the tension of the hot rolling mill 220 on the textile, T is the temperature of the pressing roller, P fis the tension of the hot rolling mill 220 on the textile, H i is the initial humidity of the textile, H f is the final humidity of the textile, H r is the rate of change of humidity of the textile, λ and β are coefficients, and τ is the time constant, which is used to adjust the rate at which the integral kernel changes with time.

[0074] The management unit 133 is used to control the operation of the hot rolling mill 220 according to the modulated working parameters, including: modulating the parameters of the hot rolling mill 220 according to the first modulation coefficient and the modulation formula, and the modulation formula is:

[0075] Z=CF×Z1

[0076] Wherein, Z is the target operating parameter of the hot rolling mill 220 after fault recovery, Z1 is the initial operating parameter of the hot rolling mill 220, and CF is the first modulation coefficient.

[0077] The hot rolling mill 220 continues to hot-roll the textile according to the adjusted parameters, namely the target working parameters, to obtain the durability of the textile hot-rolled according to the target working parameters.

[0078] In some embodiments, the management system 100 further includes a testing unit 134 and a feedback unit 135 . The testing unit 134 is used to evaluate the durability of each batch of textiles.

[0079] Exemplarily, the inspection unit 134 can provide different test conditions for the textile and test the textile. For example, the inspection unit 134 can simulate the use environment of the textile and test the durability of the textile under the corresponding environment. It can be understood that the inspection unit 134 can be a laboratory, experimental device, etc. with testing functions. Those skilled in the art can make reasonable choices according to requirements.

[0080] The feedback unit 135 is electrically connected to the management unit 133, and is also electrically connected to the inspection unit 134. The feedback unit 135 is used to generate a correction coefficient of the modulation coefficient of the corresponding batch based on the durability of the textiles of the corresponding batch and the average durability of the textiles of the historical batches.

[0081] Exemplarily, the correction coefficient is obtained according to the correction coefficient formula, and the correction coefficient formula is:

[0082] Y=N1 / N2

[0083] Where Y is the correction coefficient, N1 is the durability of the textile obtained by hot rolling according to the target working parameters, and N2 is the average durability of the textile in historical batches.

[0084] The management unit 133 is further configured to generate a target modulation coefficient according to the modulation coefficient and the correction coefficient, and modulate the working parameters of the hot rolling mill 220 according to the target modulation coefficient, and the hot rolling mill 220 operates according to the modulated working parameters.

[0085] Exemplarily, the management unit 133 generates a target modulation coefficient C according to the first modulation coefficient and the correction coefficient. 目标 , such as C 目标 =CF×Y. After the target modulation coefficient is obtained, the parameters of the hot rolling mill 220 are modulated according to the target modulation coefficient (refer to the modulation process of the first modulation coefficient described above).

[0086] In this way, after the hot rolling mill 220 fails, the parameters of the hot rolling mill 220 can be modulated according to the modulation coefficient, and the hot rolling mill 220 continues to hot-roll the textile according to the adjusted parameters to ensure that the durability of the textile material meets the predetermined quality standards.

[0087] The intelligent textile management system 100 proposed in this application can monitor key parameters in the hot rolling process in real time, and adjust the production strategy according to the first parameter and the second parameter obtained during an unexpected failure to ensure that the performance of the textile meets the expected standards, reduce uncertainties in production, and improve the overall quality of the textile.

[0088] In some embodiments, the production control module further includes a processing unit, wherein the processing unit is configured to perform the following steps:

[0089] A mapping table of the first parameter, the second parameter and the quality of the textile is established. Exemplarily, the first parameter, the second parameter and the quality of the textile of the historical batch are stored in a device with a storage function, and a related mapping table is established.

[0090] The quality of the current batch of textiles is obtained according to the first parameter of the current batch, the second parameter and the mapping table.

[0091] When a hot rolling mill fails during the production process of the current batch of textiles, the quality of the current batch of textiles can be predicted by using the first parameter of the current batch, the second parameter of the current batch, and the mapping table. For example, the first parameter and the second parameter during the hot rolling mill failure are obtained by a sensor, and the quality of the current batch of textiles is predicted based on the obtained first parameter and the second parameter and the mapping table.

[0092] Exemplarily, obtaining the quality of the current batch of textiles according to the first parameter, the second parameter and the mapping table of the current batch includes:

[0093] Obtain a matching degree between each parameter in the first parameter and the second parameter.

[0094] Based on the influence of each parameter on the quality degree, a matching degree corresponding to each parameter is assigned according to the degree of influence. For example, the greater the influence of a parameter on the quality degree, the greater the matching degree value assigned to it. Exemplarily, the matching degree value is between 0 and 1.

[0095] Obtain the absolute value of the difference between each parameter in the first parameter and the second parameter of the current batch and each parameter in the first parameter and the second parameter of the historical batch.

[0096] Subtract the parameters in the first parameters of the current batch from the parameters in the first parameters of the historical batch, such as subtracting the fault duration, and obtain their absolute values. Subtract the parameters in the second parameters of the current batch from the parameters in the second parameters of the historical batch, such as subtracting the initial humidity, and obtain their absolute values.

[0097] A similarity evaluation value is obtained based on the absolute value of the difference and the matching degree, and the quality degree of the textiles of the historical batch corresponding to the maximum similarity evaluation value is used as the quality degree of the textiles of the current batch.

[0098] The absolute value of the difference of the corresponding parameter is multiplied by the matching degree corresponding to the parameter to obtain the parameter evaluation value of the corresponding parameter, and the parameter evaluation values ​​of each parameter of the corresponding batch are summed to obtain the similarity evaluation value between the current batch and the corresponding historical batch. The quality degree of the textile of the historical batch corresponding to the maximum similarity evaluation value is used as the quality degree of the textile of the current batch.

[0099] Through the description of the above implementation methods, technicians in the relevant field can clearly understand that the diagnostic method in the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the relevant technology, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0100] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0101] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0103] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated units may be implemented in the form of hardware.

[0104] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application shall be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. An intelligent textile management system, applied to a textile production line, characterized in that: The management system comprises: An initial planning module, used to construct a basic plan based on historical order data and market trends, and the textile production line produces according to the basic plan; An inventory module, the inventory module is coupled to the initial planning module, the inventory module is used to predict the number of products that can be produced according to the inventory raw material quantity and a mapping table, and obtain the target plan according to the number of products that can be produced, the inventory product quantity and the basic plan, and the mapping table is used to map the mapping relationship between the raw material quantity and the number of products that can be produced; A production control module is coupled to the inventory module, and is used to control the textile production line to perform production according to the target plan.

2. The management system according to claim 1, characterized in that: The obtaining of the target plan according to the number of products that can be produced, the number of products in stock and the basic plan includes: Obtaining the quantity of products to be determined in the target plan according to the quantity of products in the initial plan and the quantity of products in stock; Obtain the amount of safety stock and the output capacity of the textile production line; The actual product quantity in the target plan is obtained according to the quantity of the products to be determined, the quantity of the safety stock and the output capacity of the textile production line.

3. The management system according to claim 2, characterized in that: The production control module includes: A sampling unit, the sampling unit is used to collect a first parameter and a second parameter, the first parameter is a working parameter of the hot rolling mill when the hot rolling mill fails, and the second parameter is a state parameter of the textile when the hot rolling mill fails; A modulation unit, the modulation unit is electrically connected to the sampling unit, the modulation unit generates a modulation coefficient according to the first parameter, the second parameter and the durability of the textile, and the modulation coefficient is used to modulate the working parameters of the hot rolling mill after the failure is restored; A management unit is electrically connected to the modulation unit, and is used to control the operation of the hot rolling mill according to the modulated working parameters.

4. The management system according to claim 3, characterized in that: The first parameters include the failure duration of the hot calender, the temperature of the pressure roller, the pressure of the pressure roller on the textile, and the tension of the hot calender on the textile.

5. The management system according to claim 4, characterized in that: The second parameters include the initial humidity, humidity change rate and final humidity of the textile at the time of failure.

6. The management system according to claim 5, characterized in that: The modulation unit generates a modulation coefficient according to the first parameter, the second parameter and the durability of the textile, including: The first tension coefficient is generated based on the first parameter, the second parameter and a first tension coefficient formula, where the first tension coefficient formula is: Among them, CF1 is the first tension coefficient, T f is the tension of the hot rolling mill on the textile, T is the temperature of the pressing roller, P f is the tension of the hot rolling mill on the textile, H i is the initial humidity of the textile, H f is the final humidity of the textile, H r is the humidity change rate of the textile, D is the fault duration, and θ, η, and Period are coefficients.

7. The management system according to claim 3, characterized in that: The production control module further includes a processing unit, which is configured to perform the following steps: Establishing a mapping table of the first parameter, the second parameter and the quality degree of the textile; The quality of the current batch of textiles is obtained according to the first parameter of the current batch, the second parameter and the mapping table.

8. The management system according to claim 7, characterized in that: The first parameters include the failure duration of the hot calender, the temperature of the pressure roller, the pressure of the pressure roller on the fiber material, and the tension of the hot calender on the fiber material. The second parameters include the initial moisture content, moisture change rate, and final moisture content of the fiber material.

9. The management system according to claim 7, characterized in that: The obtaining the quality of the textiles of the current batch according to the first parameter, the second parameter and the mapping table of the current batch includes: Obtaining a matching degree between each parameter in the first parameter and the second parameter; Obtaining the absolute value of the difference between each parameter in the first parameter and the second parameter of the current batch and each parameter in the first parameter and the second parameter of the historical batch; A similarity evaluation value is obtained based on the absolute value of the difference and the matching degree, and the quality degree of the textiles of the historical batch corresponding to the maximum similarity evaluation value is used as the quality degree of the textiles of the current batch.

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